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Evaluating Alternative Methods of Forecasting House Prices: A Post-Crisis Reassessment

  • William D. Larson

    ()

    (George Washington University)

This paper compares the performance of different forecasting models of California house prices. Multivariate, theory-driven models are able to outperform a theoretical time series models across a battery of forecast comparison measures. Error correction models were best able to predict the turning point in the housing market, whereas univariate models were not. Similarly, even after the turning point occurred, error correction models were still able to outperform univariate models based on MSFE, bias, and forecast encompassing statistics and tests. These results highlight the importance of incorporating theoretical economic relationships into empirical forecasting models.

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File URL: http://www.gwu.edu/~forcpgm/2010-004.pdf
File Function: Second version, 2011
Download Restriction: no

Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2010-004.

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Length: 45 pages
Date of creation: Dec 2010
Date of revision: Feb 2011
Handle: RePEc:gwc:wpaper:2010-004
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  1. Anthony Pennington-Cross, 2005. "Aggregation bias and the repeat sales price index," BIS Papers chapters, in: Bank for International Settlements (ed.), Real estate indicators and financial stability, volume 21, pages 323-335 Bank for International Settlements.
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